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https://github.com/Doctorado-ML/STree.git
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3f79d2877f
* Implement CFS/FCBF in splitter * Split Splitter class to its own file Update hyperparams table in docs Implement CFS/FCBS with max_features and variable type * Set mfs to continuous variables * Fix some tests and style issues in Splitter * Update requirements in github CI
30 lines
797 B
Python
30 lines
797 B
Python
from sklearn.datasets import make_classification
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import numpy as np
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def load_dataset(
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random_state=0, n_classes=2, n_features=3, n_samples=1500, n_informative=3
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):
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X, y = make_classification(
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n_samples=n_samples,
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n_features=n_features,
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n_informative=n_informative,
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n_redundant=0,
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n_repeated=0,
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n_classes=n_classes,
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n_clusters_per_class=2,
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class_sep=1.5,
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flip_y=0,
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random_state=random_state,
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)
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return X, y
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def load_disc_dataset(
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random_state=0, n_classes=2, n_features=3, n_samples=1500
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):
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np.random.seed(random_state)
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X = np.random.randint(1, 17, size=(n_samples, n_features)).astype(float)
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y = np.random.randint(low=0, high=n_classes, size=(n_samples), dtype=int)
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return X, y
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